Evaluating the Ecological Integrity of Structural Stand Density Management Models Developed for Boreal Conifers
Bibliographic record
Abstract
Density management decision-support systems (e.g., modular-based structural stand density management models (SSDMMs)), which are built upon the modeling platform used to develop stand density management diagrams, incorporate a number of functional relationships derived from forest production theory and quantitative ecology. Empirically, however, the ecological integrity of these systems has not been verified and hence the degree of their compliance with expected ecological axioms is unknown. Consequently, the objective of this study was to evaluate the ecological integrity of six SSDMMs developed for black spruce (Picea mariana) and jack pine (Pinus banksiana) stand-types (natural-origin and planted upland black spruce and jack pine stands, upland natural-origin black spruce and jack pine mixtures, and natural-origin lowland black spruce stands). The assessment included the determination of the biological reasonableness of model predictions by determining the degree of consistency between predicted developmental patterns and those expected from known ecological axioms derived from even-aged stand dynamics theoretical constructs, employing Bakuzis graphical matrices. Although the results indicated the SSDMMs performed well, a notable departure from expectation was a possible systematic site quality effect on the asymptotic yield-density relationships. Combining these results with confirmatory evidence derived from the literature suggest that the site-invariant self-thinning axiom may be untenable for certain stand-types.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".